基于GA-RBF神经网络逆的两电机同步控制

以多变量、非线性、强耦合的两电机同步控制系统为研究对象,提出了基于遗传算法的径向基函数(GA-RBF)神经网络逆的两电机同步控制方法。根据给定的性能指标,采用遗传算法对RBF神经中心进行优化,在此基础上串联RBF神经网络逆与两电机系统,构建复合伪线性系统。这一复杂控制对象即可解耦成转速与张力两个线性子系统,进而通过设计线性闭环调节器实现了解耦控制。实验结果表明,采用GA-RBF神经网络逆的两电机系统,对速度和张力实现了较好的解耦控制,且具有较强的抗干扰能力。

Synchronous Control of Two-Motor Based on GA-RBF Neural Network Inverse

As a multi-variable,nonlinear and strongly coupled research object,a two-motor synchronous control system was investigated in this paper.A new synchronous control strategy for two-motor system was proposed based on RBF neural network inverse with genetic algorithm.To enhance the system performance,the genetic algorithm was adopted to optimize the RBF nerve center,an optimized RBF neural network inverse and a two-motor system was connected in series to form composite preudo-linear system.This two-motor synchronous system can be decoupled into two independent linear subsystems,e.g.,speed and tention types.Moreover,a linear closed-loop adjustor was designed to control each subsystem.The experimental results show that the two-motor synchronous system can be decoupled well for speed and tension based on a GA-RBF neural network inverse system.Also,the proposed system can deal with external disturbances with strong robustness.